Classification of Chest X-ray Images using CNN for Medical Decision Support System

X-rays are a crucial tool used by healthcare professionals to diagnose a range of medical conditions. However, it is important to keep in mind that a timely and accurate diagnosis is crucial for effective patient management and treatment. While chest X-rays can provide highly precise anatomical data, manual interpretation of the images can be time-consuming and prone to errors, which can lead to delays or incorrect diagnoses. To address these issues, healthcare systems have taken steps to improve diagnostic imaging services following the impact of the COVID-19 pandemic. While deep learning-based automated systems for classifying chest X-rays have shown promise, there are still several challenges that need to be addressed before they can be widely used in clinical settings, including the lack of comprehensive and high-quality datasets. To overcome these limitations, a real-time DICOM dataset, has been converted to JPEG format to increase processing speed and improve data control. Three pre-trained models and a convolutional neural network (CNN) model with low complexity and three convolutional layers for feature extraction, along with max pooling layers and ReLU and Softmax activation functions have been implemented. With an validation accuracy of 95.05% on their CNN model using the SGD optimizer, the result has been validated using a separate, real-time unlabeled DICOM dataset of 1000 X-ray images

Structural insights into the extracellular recognition of the human serotonin 2B receptor by an antibody

Highly selective monoclonal antibodies recognizing the extracellular 3D epitope of G protein-coupled receptors represent valuable tools for elucidating receptor function and localization in the cell and show promise for a range of therapeutic applications. Here we present the structure of a complex between the human serotonin 2B receptor, captured in an active-like state, and an antibody Fab fragment, bound to the extracellular side of the receptor. The structure uncovers the mechanisms of receptor activation and of extracellular receptor recognition by antibodies

COMPARATIVE STUDY OF FIXED DOSE COMBINATION OF OLMESARTAN +AMLODIPINE VS TELMESARTAN+METOPROLOL IN STAGE I AND STAGE II HYPERTENSIVE PATIENTS AND ALSO CHECK THEIR EFFECTS ON LIPID METABOLISM

ABSTRACT: Objectives - Hypertension is the second most severe problem after diabetes in India for the mortality rate. So the primary goal to reduce the mortality is to achieving targeted blood pressure in an individual. Hypertension guidelines recommend the use of 2 agents having complementary mechanisms of action when >1 agent is needed to achieve blood pressure (BP) goals. The objectives of this study are to compare the effect of combination on BP, Lipid Profile, and also to check the safety of both combinations. Methods- This is 16 week open label, study, in which total 70 patient were enrolled. The study was carried out at outpatient department of Indira Gandhi Memorial Hospital Shirpur, Dist- dhule. We include patients which are newly diagnosed to hypertension. Permission from Institutional Human Ethical Committee was obtained. Results- combination therapies shows greater effects on lowering blood pressure than individual monotherapies. In our study we found group II combination shows greater effect than group I combination. The mean BP reduction was found to be ± 31.9/ 13.05 in group I, while in group II ±33.24/15.59 mmHg in group II from baseline after three months follow-up for systolic and diastolic respectively. While comparing lipid profile again Group II shows better results than Group I. Conclusion- These finding shows that the group II shows greater efficacy than group I in all parameter evaluated. The finding also shows that both the combinations are safe and efficacious.   Key words- Hypertension, Olmesartan, Amlodipine, Telmesartan, Metoprolol, combination therapy, Lipid Profile, Fixed Dose Combination

Structural Basis for Ligand Recognition and Functional Selectivity at Angiotensin Receptor

Angiotensin II type 1 receptor (AT(1)R) is the primary blood pressure regulator. AT(1)R blockers (ARBs) have been widely used in clinical settings as anti-hypertensive drugs and share a similar chemical scaffold, although even minor variations can lead to distinct therapeutic efficacies toward cardiovascular etiologies. The structural basis for AT(1)R modulation by different peptide and non-peptide ligands has remained elusive. Here, we report the crystal structure of the human AT(1)R in complex with an inverse agonist olmesartan (Benicar (TM)), a highly potent anti-hypertensive drug. Olmesartan is anchored to the receptor primarily by the residues Tyr-35(1.39), Trp-84(2.60), and Arg-167(ECL2), similar to the antagonist ZD7155, corroborating a common binding mode of different ARBs. Using docking simulations and site-directed mutagenesis, we identified specific interactions between AT(1)R and different ARBs, including olmesartan derivatives with inverse agonist, neutral antagonist, or agonist activities. We further observed that the mutation N111(3.35)A in the putative sodium-binding site affects binding of the endogenous peptide agonist angiotensin II but not the beta-arrestin-biased peptide TRV120027

The Importance of Ligand-Receptor Conformational Pairs in Stabilization: Spotlight on the N/OFQ G Protein-Coupled Receptor

SummaryUnderstanding the mechanism by which ligands affect receptor conformational equilibria is key in accelerating membrane protein structural biology. In the case of G protein-coupled receptors (GPCRs), we currently pursue a brute-force approach for identifying ligands that stabilize receptors and facilitate crystallogenesis. The nociceptin/orphanin FQ peptide receptor (NOP) is a member of the opioid receptor subfamily of GPCRs for which many structurally diverse ligands are available for screening. We observed that antagonist potency is correlated with a ligand's ability to induce receptor stability (Tm) and crystallogenesis. Using this screening strategy, we solved two structures of NOP in complex with top candidate ligands SB-612111 and C-35. Docking studies indicate that while potent, stabilizing antagonists strongly favor a single binding orientation, less potent ligands can adopt multiple binding modes, contributing to their low Tm values. These results suggest a mechanism for ligand-aided crystallogenesis whereby potent antagonists stabilize a single ligand-receptor conformational pair

Structural basis of ligand recognition at the human MT1 melatonin receptor

Melatonin (N-acetyl-5-methoxytryptamine) is a neurohormone that maintains circadian rhythms by synchronization to environmental cues and is involved in diverse physiological processes such as the regulation of blood pressure and core body temperature, oncogenesis, and immune function. Melatonin is formed in the pineal gland in a light-regulated manner by enzymatic conversion from 5-hydroxytryptamine (5-HT or serotonin), and modulates sleep and wakefulness by activating two high-affinity G-protein-coupled receptors, type 1A (MT$_1$) and type 1B (MT$_2$). Shift work, travel, and ubiquitous artificial lighting can disrupt natural circadian rhythms; as a result, sleep disorders affect a substantial population in modern society and pose a considerable economic burden. Over-the-counter melatonin is widely used to alleviate jet lag and as a safer alternative to benzodiazepines and other sleeping aids, and is one of the most popular supplements in the United States. Here, we present high-resolution room-temperature X-ray free electron laser (XFEL) structures of MT$_1$ in complex with four agonists: the insomnia drug ramelteon, two melatonin analogues, and the mixed melatonin–serotonin antidepressant agomelatine. The structure of MT$_2$ is described in an accompanying paper. Although the MT$_1$ and 5-HT receptors have similar endogenous ligands, and agomelatine acts on both receptors, the receptors differ markedly in the structure and composition of their ligand pockets; in MT$_1$, access to the ligand pocket is tightly sealed from solvent by extracellular loop 2, leaving only a narrow channel between transmembrane helices IV and V that connects it to the lipid bilayer. The binding site is extremely compact, and ligands interact with MT$_1$ mainly by strong aromatic stacking with Phe179 and auxiliary hydrogen bonds with Asn162 and Gln181. Our structures provide an unexpected example of atypical ligand entry for a non-lipid receptor, lay the molecular foundation of ligand recognition by melatonin receptors, and will facilitate the design of future tool compounds and therapeutic agents, while their comparison to 5-HT receptors yields insights into the evolution and polypharmacology of G-protein-coupled receptors